Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the goal of this paper is to design an effective detector capable of completing image forgery detection and localization. Specifically, relying on the designed constant high-pass filter, we first establish a well-performing CNN architecture to adaptively and automatically extract characteristics, and design a reliability fusion map (RFM) to improve localization resolution, and tamper detection accuracy. The extensive results from our empirical experiments demonstrate the effectiveness of our proposed RFM-based detector, and its better performance than other competing approaches.

中文翻译：

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可靠性融合图的法师伪造检测与定位

与基于手工驱动的特征提取不同，基于数据驱动的卷积神经网络（CNN）的算法的使用促进了多媒体取证中端对端自动伪造检测的实现。基于从不同相机型号获得的图像指纹，本文的目的是设计一种能够完成图像伪造检测和定位的有效检测器。具体来说，我们依靠设计的恒定高通滤波器，首先建立性能良好的CNN架构，以自适应地自动提取特征，然后设计可靠性融合图（RFM）以提高定位分辨率和篡改检测精度。我们的经验实验得出的广泛结果证明了我们提出的基于RFM的探测器的有效性，